Using the Web API for FLUX 1.1 [pro]: The Latest Image Generation AI Model by the Original Team of Stable Diffusion

Introduction

Previously, I wrote an article titled “Running the FLUX.1 Image ([dev]/[schnell]) Generation AI Model by Stable Diffusion’s Original Developers on a MacBook (M2).” It demonstrated the FLUX.1 image generation model from Black Forest Labs, founded by the creators of Stable Diffusion.

Now, two months later, FLUX 1.1 [pro] (codenamed Blueberry) has been released, along with public access to its web API, though it’s still in beta.

Today, we release FLUX1.1 [pro], our most advanced and efficient model yet, alongside the general availability of the beta BFL API. This release marks a significant step forward in our mission to empower creators, developers, and enterprises with scalable, state-of-the-art generative technology.

Reference: Announcing FLUX1.1 [pro] and the BFL API - Black Forest Labs

In this post, I will demonstrate how to use the FLUX 1.1 [pro] web API.

All code examples are written in Python.

Creating an Account and an API Key

Start by registering an account and logging in on the API page under the Register option.

Credits are priced at $0.01 each, and I received 50 credits upon registration (this may vary).

Based on the Pricing page, the model costs are as follows:

• FLUX 1.1 [pro]: $0.04 per image
• FLUX.1 [pro]: $0.05 per image
• FLUX.1 [dev]: $0.025 per image

Once you’re logged in, generate an API key by selecting Add Key and entering a name of your choice.

Your key will appear as shown below.

Environment Setup

I'm using macOS 14 Sonoma as my operating system.

The Python version is:

$ python --version
Python 3.12.2

To run the sample code, I installed requests:

$ pip install requests

I confirmed the installed version:

$ pip list | grep -e requests 
requests           2.31.0

To avoid hardcoding, I saved the API key as an environment variable by editing the zshrc file.

$ open ~/.zshrc

I named the environment variable BFL_API_KEY:

export BFL_API_KEY=<Your API Key Here>

Example Code

Below is the sample code from the Getting Started, with some additional comments. Ideally, it should handle errors using the status, but I left it unchanged for simplicity.

import os
import requests
import time

# Request
request = requests.post(
    'https://api.bfl.ml/v1/flux-pro-1.1',
    headers={
        'accept': 'application/json',
        'x-key': os.environ.get("BFL_API_KEY"),
        'Content-Type': 'application/json',
    },
    json={
        'prompt': 'A cat on its back legs running like a human is holding a big silver fish with its arms. The cat is running away from the shop owner and has a panicked look on his face. The scene is situated in a crowded market.',
        'width': 1024,
        'height': 768,
    },
).json()
print(request)
request_id = request["id"]

# Wait for completion
while True:
    time.sleep(0.5)
    result = requests.get(
        'https://api.bfl.ml/v1/get_result',
        headers={
            'accept': 'application/json',
            'x-key': os.environ.get("BFL_API_KEY"),
        },
        params={
            'id': request_id,
        },
    ).json()
    if result["status"] == "Ready":
        print(f"Result: {result['result']['sample']}")
        break
    else:
        print(f"Status: {result['status']}")

In this example, the prompt is:

A cat on its back legs running like a human is holding a big silver fish with its arms. The cat is running away from the shop owner and has a panicked look on his face. The scene is situated in a crowded market.

The final result format looks like this. The response time was faster compared to other APIs I’ve tested.

{
    'id': 'Request ID',
    'status': 'Ready',
    'result': {
        'sample': 'URL of the generated image',
        'prompt': 'Specified prompt'
    }
}

The sample contains the URL of the generated image, which was hosted on bflapistorage.blob.core.windows.net when I tested it.

Here's the generated image:

The result closely matches the prompt, capturing the sense of urgency.

Experimenting with Alternative Prompts

I tried different prompts to generate varied images.

Japanese Moe Heroine

Prompt: "Japanese moe heroine," using anime style.

'prompt': 'anime style, Japanese moe heroine',

Sweets from Popular Japanese Anime

Prompt: "Sweets that appear in popular Japanese anime," using anime style.

'prompt': 'anime style, sweets that appear in popular Japanese anime',

Male High School Student on a School Trip

Prompt: "Male high school student on a school trip," using anime style.

'prompt': 'anime style, male high school student on a school trip',

A Princess Playing Guitar

Prompt: "A princess playing guitar," using fantasy-art style.

'prompt': 'fantasy-art style, a princess playing guitar',

A Cute Fairy on Top of a White Laptop

Prompt: "A cute fairy on top of a white laptop," using photographic style.

'prompt': 'photographic style, a cute fairy on top of a white laptop',

28-Year-Old Japanese Woman with Black Bobbed Hair

Prompt: "28-year-old Japanese pretty woman with black bobbed hair," using photographic style.

'prompt': 'photographic style, 28-year-old Japanese pretty woman with black bobbed hair',

Hong Kong Downtown in the 1980s

Prompt: "Hong Kong downtown in the 1980s," using photographic style.

'prompt': 'photographic style, Hong Kong downtown in the 1980s',

Shinjuku Kabukicho in 2020

Prompt: "Shinjuku Kabukicho in 2020," using photographic style.

'prompt': 'photographic style, Shinjuku Kabukicho in 2020',

All of the generated images were of exceptional quality.

After generating so many high-quality AI images, reality almost feels surreal.

Conclusion

Black Forest Labs continues to innovate and enhance its AI models.

I’m looking forward to the future release of video generation capabilities.

Original Japanese Article

Stable Diffusionのオリジナル開発陣による画像生成AIモデル最新版FLUX 1.1 [pro]のWeb APIを呼んでいくつかの画像を生成してみた